Impact of Open Data Policies on Consent to Participate in Human Subjects Research: Discrepancies between Participant Action and Reported Concerns
Bibliographic record
Abstract
Research outlets are increasingly adopting open data policies as a requisite for publication, including studies with human subjects data. We investigated whether open data policies influence participants' rate of consent by randomly assigning participants to view consent forms with and without discussion of open data policies. No participants declined to participate, regardless of condition, nor did rates of drop-out vs. completion vary between conditions. Furthermore, no significant change in potential consent rates was reported when participants were openly asked about the influence of open data policies on their likelihood of consent. However, follow-up analyses indicated possible poor attention to consent forms, consistent with previous research. Moreover, thematic analysis of participants' considerations of open data policy indicated multiple considerations such as concerns regarding confidentiality, anonymity, data security, and study sensitivity. The impact of open data policies on participation raises complex issues at the intersection of ethics and scientific innovation. We conclude by encouraging researchers to consider participants as stakeholders in open data policy and by providing recommendations for open data policies in human subjects research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".